磁共振成像2025，Vol.16Issue(8)：50-57,8.DOI:10.12015/issn.1674-8034.2025.08.008
多模态MRI影像组学瘤内及瘤周特征鉴别纤维型和非纤维型脑膜瘤的研究价值
Study on value of intra-tumoral and peri-tumoral features of multimodal MRI radiomics in distinguishing fibrous from nonfibrous meningiomas
摘要
Abstract
Objective:To investigate the clinical value of T2WI-weighted imaging(T2WI),contrast-enhanced T1-weighted imaging(CE-T1WI)of the tumour body and peritumour in combination with conventional factors in identifyingfibrous and non-fibrous meningiomas.Materials and Methods:A total of 108 patients with pathologically confirmed meningiomas,including 30 fibrous meningiomas and 78 non-fibrous meningiomas,were enrolled and divided into a training set(n=76)and a validation set(n=32)in a ratio of 7:3.In the training set,1132 radiomics features were extracted from the tumour body and peri-tumour of T2WI and CE-T1WI sequences,respectively.The optimal subset of radiomics features was identified through the maximal correlation minimal redundancy method(mRMR)and the least absolute shrinkage and selection operator(LASSO).Logistic regression(LR)machine learning method to construct imaging genomics models:T2WI tumour,T2WI peritumour,CE-T1WI tumour,CE-T1WI peritumour,(T2WI+CE-T1WI)tumour,(T2WI+CE-T1WI)peritumour and(T2WI+CE-T1WI)tumour+peritumour.The conventional factors with significance(P<0.05)were screened by single-factor and multifactor logistic regression analysis methods.Then,the radiomics model with the best discriminatory efficacy was combined with the conventional factors to generate nomograms,and the diagnostic efficacy of the nomograms was evaluated by AUC,and the clinical efficacy of the model was assessed by the net benefit value of the decision curve analysis(DCA).the efficacy of this model was validated in the test set.Results:The AUC values for the T2WI tumour,T2WI peritumour,CE-T1WI tumour,CE-T1WI peritumour,(T2WI+CE-T1WI)tumour,(T2WI+CE-T1WI)peritumour and(T2WI+CE-T1WI)tumour+peritumour radiomics models in the training set were 0.925,0.803,0.837,0.872,0.902,0.894,0.908,respectively.In the test set,the corresponding values were 0.652,0.812,0.700,0.725,0.700,0.816,0.729.The AUC of the T2WI tumour radiomics model for identifying fibrous and non-fibrous meningiomas was 0.92 in the training set and 0.65 in the test set.This appeared to be an overfitting.The(T2WI+CE-T1WI)peritumour radiomics model had the highest AUC value in the test set,and the model demonstrated the best diagnostic efficacy.The discriminatory efficacy of the established(T2WI+CE-T1WI)peri-tumour radiomics model was improved from the combined model with conventional factors(T2WI signal intensity and peri-tumour oedema),and its AUCs in the training set and test set were 0.89 and 0.82,respectively.The calibration curves showed good agreement between the predicted and actual probabilities of the model's preoperative identification of fibrous and non-fibrous meningiomas,DCA results show good clinical efficacy for this model.Conclusions:Multimodal MRI radiomics models can effectively identify fibrous and non-fibrous meningiomas,and their discriminatory efficacy can be futher improved when combined with conventional factors.关键词
脑膜瘤/影像组学/分型/瘤周组织/磁共振成像Key words
meningioma/radiomics/typing/peri-tumour/magnetic resonance imaging分类
医药卫生引用本文复制引用
杨慧敏,李文鑫,姜兴岳,王倩倩,张濬韬,刘新疆..多模态MRI影像组学瘤内及瘤周特征鉴别纤维型和非纤维型脑膜瘤的研究价值[J].磁共振成像,2025,16(8):50-57,8.基金项目
Special Project for Clinical Research in Health Industry of Shanghai Municipal Health and Health Commission(No.202140266) （No.202140266）
Shanghai Pudong New Area Systematic Discipline Construction Project(No.PWZbr2022-16). 上海市卫生健康委员会卫生行业临床研究专项立项项目(编号:202140266) （No.PWZbr2022-16）
上海市浦东新区系统学科建设项目(编号:PWZbr2022-16) （编号:PWZbr2022-16）
